In medical imaging, segmentation of skull from MRI Brain images is an important task in detecting and diagnosis of brain related diseases. Due to homogeneous nature of intensities, segmenting brain and non brain tissues became an challenging task. In this paper, we proposed a method using Outlier Detection based Morphological Operations (ODMO) to segment brain from MRI head scans. First, we used outlier detection to find the threshold value followed by the morphological operations and Largest Connected Component (LCC) to extract the brain. We tested our method from the images obtained from Internet Brain Segmentation Repository (IBSR) and real time images from SBC Scan, Dindigul. In order to estimate the results of our proposed method, similarity measures and overlapping measures like Jaccard (J), Dice (D), Sensitivity (S) and Specificity (SP) are used and it is compared with manual segmented images. Our proposed technique yields exceeding results when compared with existing standard techniques such as Brain Extraction Tool (BET) and Brain Surface Extractor (BSE).

Brain Extraction, Outlier Detection, Morphological Operations, MRI Brain Image

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